This 12 month project “Responsible AI international community to reduce bias in AI music generation and analysis” will build an international community to address Responsible AI (RAI) challenges of bias in AI music generation and analysis.
Link: https://music-rai.github.io/
The aim of the project is to explore ways to tackle current over-reliance on huge training datasets for deep learning leads to AI models biased towards Western classical and pop music and marginalises other music genres. We will bring together an international and interdisciplinary team of researchers, musicians, and industry experts to make available AI tools, expertise, and datasets which improve access to marginalised music genres. This will directly benefit musicians and audiences engaging with a wider range of musical genres and benefits creative industries by offering new forms of music consumption.
The project took place Mar 2024 – Feb 2025
Publications
Bryan-Kinns, N., Sutskova, O., Fiebrink, R., Perry, P., Wilson, E., & Wszeborowska, A. (2025) Bringing People Into AI. Project Report. University of the Arts London. https://doi.org/10.58129/9mpb-h282
Bryan-Kinns, N., Wszeborowska, A., Sutskova, O., Wilson, E., Perry, P., Fiebrink, R., Vigliensoni, G., Lindell, R., Coronel, A., & Correia, N. (2025). Leveraging small datasets for ethical and responsible AI music making. In Proceedings of AudioMostly 2025 (AM.ICAD ’25). https://ualresearchonline.arts.ac.uk/id/eprint/24065/
Wilson, E., Wszeborowska, A., & Bryan-Kinns, N. (2025). A Short Review of Responsible AI Music Generation. In Proceedings of 6th Conference on AI Music Creativity (AIMC 2025), Brussels, Belgium, September 10th-12th 2025.
Artistic Mini-Projects
The aim of these artistic mini-projects is to create impact and interest in Responsible AI (RAI) concerns of bias in AI models. These mini-projects use AI tools such as low-resource AI models with small datasets and were supported by the project team and industry partners. The mini-projects showcase the challenges of bias in AI and how RAI techniques can be used to address them.
Three artistic mini-projects were selected from an open-call and funded to explore and address the bias inherent in mainstream AI models by creating music in genres often overlooked or marginalised by such systems. Congratulations to our selected projects, and thanks to all that applied to our open-call. We received an overwhelming number of responses of high quality. We held a hybrid launch event at Rich Mix, London on 13 Feb 2025, featuring music and Q&A with the artists.
Find out more about the artists, their pieces, their Responsible AI Music composition processes, and a video recording of the launch event at the online showcase: projects.musicrai.org
Project Team
Lead: Prof. Nick Bryan-Kinns (University of the Arts London, UK; UAL)
Prof. Rebecca Fiebrink (UAL)
Dr. Phoenix Perry (UAL)
Anna Wszeborowska (UAL)
Dr. Olga Sutskova (UAL)
Prof. Zijin Li (Central Conservatory of Music, China; CCoM)
Dr. Nuno Correia (Tallinn University, Estonia; TU)
Dr. Alex Lerch (Georgia Tech, USA; GT)
Prof. Sid Fels (University of British Columbia, Canada; UBC)
Dr. Gabriel Vigliensoni (Concordia University, Canada; CU)
Dr. Andrei Coronel and Dr. Raphael Alampay (Ateneo de Manila University, Philippines; AdMU)
Prof. Rikard Lindell (Dalarna University, Sweden; DU)
Project Partners
Music Hackspace (UK)
DAACI (UK)
Steinberg (Germany)
Bela (UK)
